耦合时序特征的林分类型遥感识别

高雨珊; 彭道黎; 张楠; 杨鹏辉; 杨灿灿; 陈铭捷; 陈健

doi:10.12171/j.1000-1522.20230093

耦合时序特征的林分类型遥感识别

高雨珊^1,,
彭道黎^1, ,,
张楠¹,
杨鹏辉¹,
杨灿灿^{1, 2},
陈铭捷¹,
陈健³

1.
北京林业大学森林资源和环境管理国家林草局重点实验室，北京 100083
2.
滁州学院地理信息与旅游学院，安徽滁州 239000
3.
国家林业和草原局调查规划设计院，北京 100714

基金项目: “十三五”国家重点研发计划（2016YFD0600205），安徽省高等学校科学研究重大项目（2023AH040217）。

详细信息

作者简介:
高雨珊。主要研究方向：林业遥感与信息技术。Email：gaoyushan27@bjfu.edu.cn　地址：100083 北京市海淀区清华东路35号北京林业大学林学院

责任作者:
彭道黎，教授，博士生导师。主要研究方向：森林资源监测与评价。Email：dlpeng@bjfu.edu.cn　地址：同上。

中图分类号: S771.8
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出版历程
- 收稿日期: 2023-04-24
- 修回日期: 2023-09-20
- 网络出版日期: 2023-08-24
- 刊出日期: 2024-01-24

Remote sensing classification of stand type coupled with time series features

1.
Key Laboratory of Forest Resources & Environmental Management of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
2.
School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, Anhui, China
3.
Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China

摘要

摘要:
目的
结合多源遥感数据进行特征提取，获取最优分类策略并探究时间序列特征在林分类型识别中的重要性，为遥感林分类型识别提供技术途径。
方法
结合Sentinel-2光谱特征和时间序列特征、Sentinel-1雷达后向散射特征和SRTM DEM地形特征在Google Earth Engine中进行各特征变量的提取，构建不同特征组合使用随机森林分类器进行分类并对不同分类结果进行制图输出和精度评价。
结果
（1）使用Sentinel-2时间序列光谱特征、Sentinel-1雷达后向散射特征和SRTM DEM地形特征的方案分类效果最好，总体精度为84.62%，Kappa系数为0.82；（2）在构建的5个不同特征组合方案中，多特征组合的方案分类效果优于单一特征；（3）地形特征、后向散射特征和时间序列特征对于分类结果非常重要，尤其是时间序列特征的加入能大大提升林分类型识别精度。光谱特征中短红外波段B11和B12最重要，时间序列特征中4月份和10月份为最重要的时间节点。
结论
基于多源遥感数据提取的多特征分类方案能够有效进行研究区林分类型识别，地形特征、后向散射特征和Sentinel-2时间序列特征可以作为光谱特征的有效辅助特征变量提高分类精度，使林分类型识别更为准确，尤其是时间序列特征在提高林分类型识别精度上有突出作用。
- Sentinel-2 /
- Sentinel-1 /
- 随机森林 /
- Google Earth Engine (GEE) /
- 时间序列特征
Abstract:
Objective
This paper aims to combine multi-source remote sensing data for feature extraction to determine the most effective classification strategy. Additionally, we investigated the significance of time series features in identifying forest types, offering a technical approach for remote sensing-based forest type identification.
Method
This study combined Sentinel-2 spectral features, time series features, Sentinel-1 radar backscatter features, and SRTM DEM terrain features to extract various feature variables using Google Earth Engine. Multiple feature combinations were constructed and classified using the random forest classifier. Subsequently, mapping output and accuracy evaluations were performed on the resulting classifications.
Result
(1) The scheme that incorporates Sentinel-2 time series features, Sentinel-1 radar backscatter features, and SRTM DEM terrain features exhibited the highest classification accuracy, achieving an overall accuracy of 84.62% and a Kappa coefficient of 0.82. (2) Among the five constructed feature combination schemes, the multi-feature combination scheme demonstrated superior classification performance compared with individual feature. (3) Terrain features, radar backscatter features, and time series features significantly influenced the classification results. The inclusion of time series features notably enhanced the accuracy of forest type identification. Among the spectral features, the shortwave infrared bands B11 and B12 were the most critical, while April and October were identified as the most important time nodes within the time series features.
Conclusion
The multi-feature classification scheme, which combines data from various remote sensing sources, is proved to be effective in accurately identifying forest types in the study area. SRTM DEM terrain features, Sentinel-1 radar backscatter features, and Sentinel-2 time series features serve as valuable complementary indicators to spectral features, enhancing classification accuracy. Time series features, in particular, play a significant role in improving the accuracy of forest type identification.
- Sentinel-2 /
- Sentinel-1 /
- random forest /
- Google Earth Engine (GEE) /
- time series feature

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图 1 研究区地理位置与地形图

Figure 1. Geographical location and topographic map of the study area

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图 2 研究区样本点分布图

Figure 2. Distribution of sample points in the study area

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图 3 技术路线图

Figure 3. Technical workflow of the study

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图 4 方案1 ~ 4特征贡献率及特征累计贡献率

SLO.坡度；ELE.海拔；ASP.坡向。SLO, slope; ELE, elevation; ASP, aspect.

Figure 4. Feature contribution rate and feature cumulative contribution rate of schemes 1−4

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图 5 方案5特征贡献率及特征累计贡献率

Figure 5. Feature contribution rate and feature cumulative contribution rate of scheme 5

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图 6 方案5各时期排名前30%的光谱特征个数

Figure 6. Number of spectral features of top 30% ineach period of scheme 5

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图 7 方案5排名前30%的光谱特征及数量

Figure 7. Spectral features and quantities of the top 30% of scheme 5

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图 8 5种方案的分类结果

Figure 8. Classification results of 5 schemes

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图 9 5种方案的混淆矩阵

Figure 9. Confusion matrix of 5 schemes

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图 10 5种方案分类精度对比

Figure 10. Comparison of classification accuracy of 5 schemes

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表 1 Sentinel-2光谱特征集描述

Table 1 Description of Sentinel-2 spectral feature set

特征类型 Feature type	特征名称 Feature name	特征说明/计算公式 Feature description/calculation formula
波段 Band	B2	波长490 nm，蓝色 Wave length 490 nm, blue
	B3	波长560 nm，绿色 Wave length 560 nm, green
	B4	波长665 nm，红色 Wave length 665 nm, red
	B5	波长705 nm，红边波段 Wave length 705 nm, red edge band
	B6	波长749 nm，红边波段 Wave length 749 nm, red edge band
	B7	波长783 nm，红边波段 Wave length 783 nm, red edge band
	B8	波长842 nm，近红外 Wave length 842 nm, near infrared
	B8A	波长865 nm，近红外 Wave length 865 nm, near infrared
	B11	波长1610 nm，短波红外 Wave length 1610 nm, shortwave infrared
	B12	波长2190 nm，短波红外 Wave length 2190 nm, shortwave infrared
植被指数 Vegetation index	归一化植被指数 Normalized difference vegetation index (NDVI)	$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}}$
	绿波段归一化差值植被指数 Green normalized difference vegetation index (GDNVI)	$\mathrm{G}\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}-{\rho }_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}} + {\rho }_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}}$
	差值植被指数 Difference vegetation index (DVI)	$\mathrm{D}\mathrm{V}\mathrm{I}={\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}$
	增强植被指数 Enhanced vegetation index (EVI)	$\mathrm{E}\mathrm{V}\mathrm{I}=2.5\times \left (\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}} + 6{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}-7.5{\rho }_{\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}} + 1}\right)$
	土壤调节植被指数 Soil-adjusted vegetation index (SAVI)	$\mathrm{S}\mathrm{A}\mathrm{V}\mathrm{I}=1.5\times \left (\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}} + 0.5}\right)$
	红−短波红外波段差值 Red-SWIR band difference (Red-SWIR)	$\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}={\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}-{\rho }_{\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}1}$
	比值植被指数 Ratio vegetation index (RVI)	$\mathrm{R}\mathrm{V}\mathrm{I}=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}}{{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}}$
	红边归一化植被指数1 Red-edge normalized difference vegetation index 1 (NDVIre1)	$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\mathrm{r}\mathrm{e}1=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}}$
	红边归一化植被指数2 Red-edge normalized difference vegetation index 2 (NDVIre2)	$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\mathrm{r}\mathrm{e}2=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}2}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}2}}$
	红边归一化植被指数3 Red-edge normalized difference vegetation index 3 (NDVIre3)	$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\mathrm{r}\mathrm{e}3=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}3}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}3}}$
	归一化红边 1 Normalized difference red-edge 1 (NDre1)	$\mathrm{N}\mathrm{D}\mathrm{r}\mathrm{e}1=\dfrac{{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}2}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}}{{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}2} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}}$
	归一化红边2 Normalized difference red-edge 2 (NDre2)	$\mathrm{N}\mathrm{D}\mathrm{r}\mathrm{e}2=\dfrac{{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}3}-{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}}{{\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}3} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}}$
	归一化近红外 Normalized near infra-red (Norm-NIR)	$\mathrm{N}\mathrm{o}\mathrm{r}\mathrm{m}{\text{-}}\mathrm{N}\mathrm{I}\mathrm{R}=\dfrac{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}}{{\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}} + {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}} + {\rho }_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}}$
注： $\; {\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}$ 为近红外波段的反射值； $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}$ 为红光波段的反射值； $\; {\rho }_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}$ 为绿光波段的反射值； $\; {\rho }_{\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}}$ 为蓝光波段的反射值； $\; {\rho }_{\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}1}$ 为短波红外波段（此处为B11波段）的反射值； $\; {\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}}$ 为近红外波段（此处为B8A）波段的反射值； $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}$ 为红边波段1（此处为B5波段）的反射值； $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}2}$ 为红边波段2（此处为B6波段）的反射值； $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}3}$ 为红边波段3（此处为B7波段）的反射值。Notes: $\; {\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}}$ is the reflection value in the near infrared band; $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}}$ is the reflection value of the red band; $\; {\rho }_{\mathrm{G}\mathrm{r}\mathrm{e}\mathrm{e}\mathrm{n}}$ is the reflection value of the green band; $\; {\rho }_{\mathrm{B}\mathrm{l}\mathrm{u}\mathrm{e}}$ is the reflection value of the blue band; $\; {\rho }_{\mathrm{S}\mathrm{W}\mathrm{I}\mathrm{R}1}$ is the reflection value of the shortwave infrared band (which refers to the B11 band here); $\; {\rho }_{\mathrm{N}\mathrm{I}\mathrm{R}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{w}}$ is the reflection value of the near infrared band (which refers to the B8A band here); $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}1}$ is the reflection value of the red edge band band 1 (which refers to the B5 band here); $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}2}$ is the reflection value of the red edge band 2 (which refers to the B6 band here); $\; {\rho }_{\mathrm{R}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{d}\mathrm{g}\mathrm{e}3}$ is the reflection value of the red edge band 3 (which refers to the B7 band here).

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表 2 分类系统

Table 2 Classification system

一级分类 First level classification	二级分类 Secondary level classification
林地 Forest land	白桦林 Betula platyphylla forest
	落叶松林 Larix gmelinii forest
	山杨林 Populus davidiana forest
	蒙古栎林 Quercus mongolica forest
	旱柳林 Salix matsudana forest
非林地 Non-forest land	荒地/未利用地 Wasteland/unused land
	耕地 Farmland
	城镇/道路 Town/road
	水域 Water area

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表 3 5种分类方案的特征组合

Table 3 Feature combinations of five classification schemes

序号 No.	特征组合 Feature combination	变量个数 Number of variable
1	Sentinel-2光谱特征 Sentinel-2 spectral feature	23
2	Sentinel-2光谱特征 + Sentinel-1雷达后向散射特征 Sentinel-2 spectral feature + Sentinel-1 SAR backscattering feature	25
3	Sentinel-2光谱特征 + DEM地形特征 Sentinel-2 spectral feature + DEM terrain feature	26
4	Sentinel-2光谱特征 + Sentinel-1雷达后向散射特征 + DEM地形特征 Sentinel-2 spectral feature + Sentinel-1 SAR backscattering feature + DEM terrain feature	28
5	Sentinel-2时间序列光谱特征 + Sentinel-1雷达后向散射特征 + DEM地形特征 Sentinel-2 time-series spectral feature + Sentinel-1 SAR backscattering feature + DEM terrain feature	166

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表 4 方案5中排名前30%的特征变量

Table 4 Top 30% characteristic variables in schemes 5

变量排名 Ranking of variable	特征变量名称 Feature variable name	变量排名 Ranking of variable	特征变量名称 Feature variable name
1	slope	26	NDVIre3_Oct.
2	B12_Apr.	27	B11_Aug.
3	B11_Oct.	28	GNDVI_Apr.
4	VV	29	GNDVI_Sept.
5	B12_May	30	GNDVI_Jul.
6	B12_Jun.	31	RVI_Sept.
7	B11_May	32	B11_Jul.
8	B12_Jul.	33	RVI_Jun.
9	elevation	34	B11_Apr.
10	B11_Sept.	35	EVI_Oct.
11	EVI_Apr.	36	EVI_May
12	VH	37	NDVIre2_Sept.
13	NDre1_Apr.	38	EVI_Jun.
14	GNDVI_Oct.	39	B12_Sept.
15	NDre2_Apr.	40	NDVIre3_Aug.
16	NDVIre1_Apr.	41	B6_May
17	NDVI_Oct.	42	aspect
18	B12_Oct.	43	NDVI_Apr.
19	B7_Apr.	44	RedSWIR_Oct.
20	NDVIre3_Sept.	45	B8_Apr.
21	Norm-NIR_Apr.	46	EVI_Jul.
22	B7_Oct.	47	NDVIre2_Apr.
23	B6_Apr.	48	B11_Jun.
24	B5_May	49	DVI_May
25	Norm-NIR_Oct.	50	B8A_Oct.

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